What’s the difference between search marketing automation and machine learning? Are they interchangeable?
What do search marketers fear most about these topics? How can they fully embrace them?
These are just a few common questions our Lisa Little, a 2020 Search Engine Land Awards Search Marketer of the Year, gets asked all the time, so read on as she answers them.
What’s the Difference Between Search Marketing Automation and Machine Learning?
Automation: This is a wide range of technologies that reduce human intervention in processes.
Search automation examples are scripts, bidding technology, auto-applied recommendations, automated audits, budget-pacing tools, optimization score recommendations and management or reporting platforms.
The tools to complete automated actions are done by a machine instead of a human. Automated aspects act or run behind the scenes on cruise control without someone in the driver’s seat pushing buttons. (Note: The driver tells the car what speed to maintain and sets cruise control before the automation takes over.)
Machine Learning: This is the study of computer algorithms that automatically improve through experience, and using data is seen as part of artificial intelligence.
Today, machine learning comes into play with responsive search ads, real-time auctions, quality scores, smart campaigns, smart bidding or time decay and with data-driven attribution models, ad extensions, a customized SERP experience and more.
Machine learning supports the search account aspects that require so much real-time data that human minds can’t comprehend at the sheer speed required to compute and translate data. Machine learning is constantly used without advertisers’ control, approval or understanding. Search marketers choose to leverage and act upon data in a scalable and agile way through efficient automations. Embrace machine learning along with automation, and you’ll produce amazing results.
What Are Marketers’ Fears About Search Marketing Automation and Machine Learning?
Search marketers are hesitant to use automation and machine learning because of the lack of control and transparency; brand advertisers are typically slow to adopt, or don’t approve; they’re unsure about the automation types available and are concerned automation will replace jobs; and they’re confused about how to learn about them (or are unwilling to do so).
Though, the reality is if you’re in search marketing, automation and machine learning are happening around you so you should explore them, not ignore them! Brands that don’t evolve with customer needs, expectations and behaviors quickly fail and ultimately become extinct.
How Should Marketers Embrace Their Fears?
Use machine learning to keep up with demands as it allows you to adapt quickly and seamlessly based on the data and feedback you’re receiving. Simply put, automation and machine learning are required to keep up an accelerated search pace, particularly with the unexpected, epic and volatile behavior shifts over the past year.
Look fear in the face and trudge ahead through the challenges. Think of this comparison: Search machine learning is like a robot vacuum cleaner. It cleans your floors for you. But first it must learn your floor plan and understand your performance expectations. It requires regular charging, cleaning filters and other maintenance. The best thing about a robot vacuum is you can spend that hour you used to vacuum your house in a more meaningful way. Now you have time to spend with your family, work out or cook dinner – and the chore gets done. The robot vacuum cleaner doesn’t replace you but allows for more productive and efficient floor cleaning so you can focus your time elsewhere. Of course, you still have to check up on the robot vacuum cleaner and troubleshoot and maintain it, but you can use it to your advantage.
Even with a robot vacuum, you still have to do some of the floor work yourself, whether that’s cleaning up a spill or cleaning those tight spaces a robot vacuum cleaner simply can’t reach. The concept is the same with search machine learning. Find ways to leverage automation to complement your overall program and results. At times you will need to do manual upkeep or repair, but that should work in tandem with all aspects of the account.
What Are Marketers’ Search Automation and Machine Learning Do’s and Don’ts? (Handy Checklist)
Take it one step at a time. Dip your toe in with an automated bidding strategy to start. Most smart bidding strategies are generally straightforward, and selection depends on what goals you’re trying to achieve. To start, go with the simplest goal: Maximize clicks.
Check in daily and learn to love data. While we can’t physically see the data going into the automation or making the machine work, we can still know and appreciate the value of what we can’t see. Take a test, tweak and learn approach to analyzing data results. Stay curious!
Own your research and customize how you use automation. Google or Microsoft may say certain tools or automation are better, but that doesn’t mean that’s what’s best for your business or campaign goals every single time. For example, automated ad copy creation might not be the best because it’s harder for machines to understand the best value propositions. However, leveraging responsive search ads automation is strong, outperforms other ad formats and is required to keep up with all the consumer buying signals. Ultimately, you decide how automation will work in a brand’s favor.
Set baselines and frequently check performance and volume against them. Make sure you’re looking at search performance and volume or impact in your marketing efforts beyond paid search. The performance story should not be siloed to any one specific channel but instead is most effective when looking at things like conversions, revenue, sentiment, consumer behavior and messaging across the full marketing mix.
Rely on automation 100%. Automation needs human input, checkups, troubleshooting, maintenance and continual monitoring. Don’t turn on the machine and walk away from it. In the post-setup phase, inspect results and troubleshoot. Once you have statistically significant volume and understand the results, you can begin optimizing and course correcting with campaign adjustments.
Bite off more than you can chew. Because you need to regularly check, maintain, troubleshoot, analyze and explain what the automation is doing for your campaigns, don’t overwhelm the system by having too many things (especially conflicting automations in multitiered brands) going at one time. Troubleshooting can get complicated with too many added variables.
Approach automation as one size fits all. Different campaigns or groups will require different approaches to automation. Structure by goal so you can apply the best rule or setting to a group. For example, setting cost per action (CPA) or return on ad spend (ROAS) target goals may work for certain groups, whereas click or position-based rules may work better for other campaigns. As a general rule of thumb, Max Conversions (with a set target CPA) should be used when the value of the conversion is more static. Max Conversion Value (MCV), a set target ROAS, should be used whenever the conversion value is variable – if the value of a lead or sale can vary from conversion to conversion.
Forget to watch cost per clicks (CPCs). Automated strategies increase cost per clicks (CPCs) rapidly, but conversions don’t always follow. Actions fluctuate with search behavior, search volume, auction competition and so on.
As you embrace search marketing automation and machine learning, understand what they are, how they work together, when to leverage each type and how to measure their impact and success. Stay open, be willing to explore, innovate, impact and adjust automation and machine learning along the way to create the best combination for your business.
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